An unsupervised text-to-speech synthesis (TTS) system learns to generate
speech waveforms corresponding to any written sentence in a language by
observing: 1) a collection of untranscribed speech waveforms in that language;
2) a collection of texts written in that language without access to any
transcribed speech. Developing such a system can significantly improve the
availability of speech technology to languages without a large amount of
parallel speech and text data. This paper proposes an unsupervised TTS system
based on an alignment module that outputs pseudo-text and another synthesis
module that uses pseudo-text for training and real text for inference. Our
unsupervised system can achieve comparable performance to the supervised system
in seven languages with about 10-20 hours of speech each. A careful study on
the effect of text units and vocoders has also been conducted to better
understand what factors may affect unsupervised TTS performance. The samples
generated by our models can be found at
this https URL, and our code can be found
at this https URL.

该论文提出了一种基于对齐模块输出伪文本和另一个合成模块使用伪文本进行训练和实际文本进行推断的无监督文本转语音综合系统，可以在每种语言的 10-20 小时语音中实现与监督系统相当的性能，并开展了对文本单元和声码器影响的研究。